Abstract: Antibiotic resistance is a mounting problem at the global scale that compromises the use of these drugs as our main defense against microbial infections. The antibiotics themselves act as a selective pressure for resistance, and the present solution of developing new antibiotic classes only delays the problem until new resistance emerges. My goal is to develop entirely new strategies to fight pathogenic bacteria by targeting the social interactions involved in pathogenesis. The goal is motivated by the realization that most pathogenic bacteria are not isolated organisms, but rather live in multicellular communities called biofilms where cell-cell interactions are essential. Our recent applications of social evolutionary theory to microbiology have already shown that biofilm formation, quorum sensing and virulent secretions are highly dependent on interactions among cells and that the fate of cooperative interactions is challenged by the presence of competing strains. Therefore, I hypothesize that therapies that target social interactions can reduce the virulence of bacterial populations without creating strong selection for resistance. I will test this hypothesis in the bacterium Pseudomonas aeruginosa, an opportunistic human pathogen notorious for infecting the lungs of cystic fibrosis patients by forming antibiotic resistant biofilms. The formation of robust biofilms requires well-regulated secretion of rhamnolipid biosurfactants, which are self-produced dispersants that play a major role in shaping biofilm 3-D structure. I will investigate the conditions that lead to unregulated rhamnolipid secretion as potential strategies for self-induced biofilm dispersal. For the period of this award I will carry out three complementary research avenues that will combine quantitative-experimental and computational methods: (1) I will characterize the dynamic response of the quorum sensing regulation of biosurfactant secretion in P. aeruginosa. I will carry this out by selectively deleting genes in the regulatory pathway and measuring system response using reporter fusions. (2) I will develop the next generation of realistic 3-D computational biofilm models. I will apply these models to rationally design strategies that induce self-promoted biofilm dispersal. (3) I will quantify the networks of social interactions and test experimentally strategies that disperse biofilms by perturbing those interactions. These studies expand the applications of quantitative social evolution to molecular and cell biology, and will provide for the first time a systems view of microbial groups that integrates the dynamic observations of genetic and phenotypic diversity among cells with the importance of cellular cooperation. The project leverages my unique expertise at the interface of engineering, systems biology and evolution, and applies this expertise towards new therapies against microbial infection.